



'''simple docstring'''



import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
							import torch


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


lowerCAmelCase_  :							int             =    logging.get_logger(__name__)
lowerCAmelCase_  :							List[Any]             =    {'vocab_file': 'spiece.model'}

lowerCAmelCase_  :							List[Any]             =    {
    'vocab_file': {
        'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
        'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
    }
}

lowerCAmelCase_  :							Any             =    {
    'AI-Sweden/gpt-sw3-126m': 2048,
    'AI-Sweden/gpt-sw3-350m': 2048,
    'AI-Sweden/gpt-sw3-1.6b': 2048,
    'AI-Sweden/gpt-sw3-6.7b': 2048,
    'AI-Sweden/gpt-sw3-20b': 2048,
}
class 						lowerCamelCase_				(       snake_case_      ):
						_lowerCAmelCase  : Union[str, Any]						  =					VOCAB_FILES_NAMES
						_lowerCAmelCase  : Dict						  =					PRETRAINED_VOCAB_FILES_MAP
						_lowerCAmelCase  : Any						  =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCAmelCase  : List[Any]						  =					['input_ids', 'attention_mask']







						def __init__(	self :   str					,     lowerCAmelCase__ :   Dict					,     lowerCAmelCase__ :   Tuple=False					,     lowerCAmelCase__ :   Union[str, Any]=False					,     lowerCAmelCase__ :   Optional[int]=False					,     lowerCAmelCase__ :   Dict=None					,     lowerCAmelCase__ :   Tuple=None					,     lowerCAmelCase__ :   Any=None					,     lowerCAmelCase__ :   str=None					,     lowerCAmelCase__ :   Optional[Dict[str, Any]] = None					,     **lowerCAmelCase__ :   Optional[Any]					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[str]         =      {} if sp_model_kwargs is None else sp_model_kwargs

													SCREAMING_SNAKE_CASE       :   Any         =      kwargs.get('''name_or_path'''					)
													if name_or_path is None:
																				logger.warning(
																				    '''name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'''
																				    ''' you are testing the model, this can safely be ignored'''					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      '''None'''

													# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
													SCREAMING_SNAKE_CASE       :   List[Any]         =      '''<|endoftext|>''' if eos_token is None else eos_token
													SCREAMING_SNAKE_CASE       :   List[str]         =      '''<unk>''' if unk_token is None else unk_token
													if "gpt-sw3-7b" in name_or_path:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      unk_token if pad_token is None else pad_token
																				SCREAMING_SNAKE_CASE       :   List[str]         =      eos_token if bos_token is None else bos_token
													else:
																				SCREAMING_SNAKE_CASE       :   List[str]         =      '''<pad>''' if pad_token is None else pad_token
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      '''<s>''' if bos_token is None else bos_token

													super().__init__(
													    do_lower_case=lowerCAmelCase__					,     remove_space=lowerCAmelCase__					,     keep_accents=lowerCAmelCase__					,     bos_token=lowerCAmelCase__					,     eos_token=lowerCAmelCase__					,     unk_token=lowerCAmelCase__					,     pad_token=lowerCAmelCase__					,     sp_model_kwargs=self.sp_model_kwargs					,     **lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      do_lower_case
													SCREAMING_SNAKE_CASE       :   str         =      remove_space
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      keep_accents
													SCREAMING_SNAKE_CASE       :   str         =      vocab_file

													SCREAMING_SNAKE_CASE       :   Dict         =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
													self.sp_model.Load(lowerCAmelCase__					)

													# Used for whitespace normalization in input texts
													# fmt : off
													SCREAMING_SNAKE_CASE       :   str         =      {''' ''', ''' ''', ''' ''', ''' ''', ''' ''', '''　''', ''' ''', ''' ''', ''' ''', ''' ''', '''￼''', ''''''}
													# fmt : on

													# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
													SCREAMING_SNAKE_CASE       :   List[str]         =      re.compile(
													    F"""[{"".join(map(lowerCAmelCase__					,     list(range(0					,     9					)					) + list(range(11					,     32					)					) + list(range(1_27					,     1_60					)					) + [1_60, 1_73, 82_03]					)					)}]"""					)







						def __getstate__(	self :   int					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.__dict__.copy()
													SCREAMING_SNAKE_CASE       :   Tuple         =      None
													return state







						def __setstate__(	self :   Optional[int]					,     lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      d

													# for backward compatibility
													if not hasattr(self					,     '''sp_model_kwargs'''					):
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      {}

													SCREAMING_SNAKE_CASE       :   List[str]         =      spm.SentencePieceProcessor(**self.sp_model_kwargs					)
													self.sp_model.Load(self.vocab_file					)







						@property
						# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													return len(self.sp_model					)







						def        __lowercase     (	self :   Optional[Any]					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.non_printing_characters_re.sub(''''''					,     lowerCAmelCase__					)

													# Normalize whitespaces
													SCREAMING_SNAKE_CASE       :   Tuple         =      ''''''.join([char if char not in self.whitespaces else ''' ''' for char in text]					)

													# NFC Unicode normalization
													SCREAMING_SNAKE_CASE       :   Any         =      unicodedata.normalize('''NFC'''					,     lowerCAmelCase__					)
													return text







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   str					,     **lowerCAmelCase__ :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.preprocess_text(lowerCAmelCase__					)
													return self.sp_model.encode(lowerCAmelCase__					,     out_type=lowerCAmelCase__					)







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   str					):

													"""simple docstring"""
													return self.sp_model.PieceToId(lowerCAmelCase__					)







						def        __lowercase     (	self :   str					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													return self.sp_model.IdToPiece(lowerCAmelCase__					)







						@staticmethod
						def        __lowercase     (	lowerCAmelCase__ :   str					):

													"""simple docstring"""
													return out_string







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      []
													SCREAMING_SNAKE_CASE       :   Dict         =      ''''''
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
													for token in tokens:
																				# make sure that special tokens are not decoded using sentencepiece model
																				if token in self.all_special_tokens:
																											# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
																											if not prev_is_special:
																																		out_string += " "

																											out_string += self.sp_model.decode(lowerCAmelCase__					) + token
																											SCREAMING_SNAKE_CASE       :   Tuple         =      True
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      []
																				else:
																											current_sub_tokens.append(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      False
													out_string += self.sp_model.decode(lowerCAmelCase__					)

													return out_string







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {self.convert_ids_to_tokens(lowerCAmelCase__					): i for i in range(self.vocab_size					)}
													vocab.update(self.added_tokens_encoder					)
													return vocab







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   str					,     lowerCAmelCase__ :   Optional[str] = None					):

													"""simple docstring"""
													if not os.path.isdir(lowerCAmelCase__					):
																				logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""					)
																				return
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      os.path.join(
													    lowerCAmelCase__					,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

													if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase__					) and os.path.isfile(self.vocab_file					):
																				copyfile(self.vocab_file					,     lowerCAmelCase__					)
													elif not os.path.isfile(self.vocab_file					):
																				with open(lowerCAmelCase__					,     '''wb'''					) as fi:
																											SCREAMING_SNAKE_CASE       :   List[str]         =      self.sp_model.serialized_model_proto()
																											fi.write(lowerCAmelCase__					)

													return (out_vocab_file,)







						def        __lowercase     (	self :   Union[str, Any]					,     lowerCAmelCase__ :   Union[str, List[str]]					,     lowerCAmelCase__ :   Union[str, bool] = False					):

													"""simple docstring"""

													if isinstance(lowerCAmelCase__					,     lowerCAmelCase__					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      self.preprocess_text(lowerCAmelCase__					)
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      self.sp_model.encode(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   List[Any]         =      [self.preprocess_text(lowerCAmelCase__					) for t in text]
																				SCREAMING_SNAKE_CASE       :   Dict         =      self.sp_model.encode(lowerCAmelCase__					)

													if return_tensors is True or return_tensors == "pt":
																				SCREAMING_SNAKE_CASE       :   List[str]         =      torch.tensor(lowerCAmelCase__					)

													return token_ids







						def        __lowercase     (	self :   int					,     lowerCAmelCase__ :   Union[int, List[int]]					):

													"""simple docstring"""
													return self.sp_model.decode(lowerCAmelCase__					)







						def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   "Conversation"					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
													SCREAMING_SNAKE_CASE       :   Dict         =      (
													    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowerCAmelCase__					) + F"""{self.bos_token}Bot:"""
													)
													return self.encode(text=lowerCAmelCase__					)









'''simple docstring'''



lowerCAmelCase_  :							List[Any]             =    [0, 2, 4, 6, 8]
lowerCAmelCase_  :							str             =    [1, 3, 5, 7, 9]
def        UpperCAmelCase (    A	:					int       ,		A	:					int       ,		A	:					list[int]       ,		A	:					int					):
							if remaining_length == 0:
														if digits[0] == 0 or digits[-1] == 0:
																					return 0

														for i in range(length // 2 - 1       ,		-1       ,		-1					):
																					remainder += digits[i] + digits[length - i - 1]

																					if remainder % 2 == 0:
																												return 0

																					remainder //= 10

														return 1

							if remaining_length == 1:
														if remainder % 2 == 0:
																					return 0

														SCREAMING_SNAKE_CASE       :   List[Any]         =      0
														for digit in range(10					):
																					SCREAMING_SNAKE_CASE       :   Optional[Any]         =      digit
																					result += reversible_numbers(
																					    0       ,		(remainder + 2 * digit) // 10       ,		A       ,		A					)
														return result

							SCREAMING_SNAKE_CASE       :   List[str]         =      0
							for digita in range(10					):
														SCREAMING_SNAKE_CASE       :   int         =      digita

														if (remainder + digita) % 2 == 0:
																					SCREAMING_SNAKE_CASE       :   str         =      ODD_DIGITS
														else:
																					SCREAMING_SNAKE_CASE       :   List[str]         =      EVEN_DIGITS

														for digita in other_parity_digits:
																					SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      digita
																					result += reversible_numbers(
																					    remaining_length - 2       ,		(remainder + digita + digita) // 10       ,		A       ,		A       ,		)
							return result
def        UpperCAmelCase (    A	:					int = 9					):
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      0
							for length in range(1       ,		max_power + 1					):
														result += reversible_numbers(A       ,		0       ,		[0] * length       ,		A					)
							return result


if __name__ == "__main__":
							print(f'{solution() = }')




